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Preferences for public involvement in health service decisions: a comparison between best-worst scaling and trio-wise stated preference elicitation techniques

机译:公众参与卫生服务决策的偏好:最差扩展和三态陈述偏好诱导技术之间的比较

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摘要

Stated preference elicitation techniques, such as discrete choice experiments and best-worst scaling, are now widely used in health research to explore the public’s choices and preferences. In this paper, we propose an alternative stated preference elicitation technique, which we refer to as ‘trio-wise’. We explain this new technique, its relative advantages, modeling framework, and how it compares to the best-worst scaling method. To better illustrate the differences and similarities, we utilize best-worst scaling Case 2, where individuals make best and worst (most and least) choices for the attribute levels that describe a single profile. We demonstrate this new preference elicitation technique using an empirical case study that explores preferences among the general public for ways to involve them in decisions concerning the health care system. Our findings show that the best-worst scaling and trio-wise preference elicitation techniques both retrieve similar preferences. However, the capability of our trio-wise method to provide additional information on the strength of rank preferences and its ability to accommodate indifferent preferences lead us to prefer it over the standard best-worst scaling technique.
机译:陈述的偏好激发技术,例如离散选择实验和最差标度,现已广泛用于健康研究中,以探索公众的选择和偏好。在本文中,我们提出了另一种陈述式偏好激发技术,我们称之为“三重”。我们将说明这项新技术,其相对优势,建模框架,以及如何将其与最差的缩放方法进行比较。为了更好地说明差异和相似性,我们使用最差缩放案例2,在此情况下,个人为描述单个配置文件的属性级别做出了最佳和最差(最大和最小)选择。我们使用一个经验案例研究来演示这种新的偏好激发技术,该案例研究探索了公众之间的偏好,以寻求使他们参与有关医疗保健系统决策的方法。我们的发现表明,最差的定标和三向偏好激发技术都可以检索相似的偏好。但是,我们的三级方法提供有关等级偏好强度的其他信息的能力及其适应不同偏好的能力使我们比标准的最差标度技术更喜欢它。

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